Traffic Shape Recognition Based On Contour | ADAS Technology

ADAS Technology and Its Issues

Discuss About The Traffic Shape Recognition Based On Contour.

Artificial Intelligence (AI) has been contributing in the development of the life living of the individuals through facilitating the automation in the real life and many applications of the AI in the real world. More or less AI can be applicable in most of the sectors in the real world and so is in the automobile sector that has been advanced with the technology and resulted in many beneficial advancement. Most of the accident cases at the roads occur because of the negligence of the drivers and ADAS is capable of predicting the scenarios and presenting solution for eliminating these challenges through the real-time data collection and execution. Many automated cars have been utilizing the LiDAR (Light Detection and Ranging), RADAR (Radio Detection and Ranging), and IR (infrared) sensors, cameras, and Ultrasonic cameras. However, the performance of the ADAS technology has not been much reliable until the date and this report aims at highlighting o that perspective and proposing solutions those could be utilized for eliminating the problems. It can be stated that the automobile industry’s les in the option of presenting driverless cars with zero error and 100% efficiency. The objective of this project is to eliminate the identified issues and propose effective solutions those could be applied for the enhancement in the performance of the ADAS technology and make it more robust, efficient, and accurate.

ADSAS can be represented as encapsulation of various systems comprised of IoT (Internet of Things) and V2V (vehicle-to-vehicle) wireless communication systems, vision camera systems, and sensor technologies (kale and Mahajan 2015). The ADAS system comprised of very vast sections those cannot be covered in a single file and hence, this report emphasizes on two important visions including the drowsiness alert system and traffic sign recognition system. In these both the scenarios, the input data is recorded in the form of the image and/ or video and passed to the processing unit for the real-time data execution (Villalon Torres & Flores 2017). The processor will be making the computation on the basis of the algorithms embedded within it and the output decision will be carried out accordingly. Major five stages of the computer vision pipeline include the “data processing, objects segmentation and detection, feature extraction, classification and evaluation (Zeng et al. 2015).” The every stage has its different role including from the collection of data, processing of data and extraction features. The features those have been extracted will be passing through different classifiers in manner to for the training (Choi, Song & Lee 2018). “k-Nearest Neighbor, Naïve Bayes, Linear classifier, Decision Trees, Random Forests, Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), AdaBoost and ensemble of various classifiers” comprised of the main classifiers within the computer vision research (Nguyenn, Ryong and Kyu 2014). (Jung et al. 2018) have noticed it that the approaches have been not very good for the performance of the ADAS technology considering the conventional computer vision. Previously, the recognition of the traffic sign was delivered through the traditional conventional computer system however; te current approaches can be delivered for the identification of the colors, edges, and extraction of the smaller objects (Sun et al. 2015). In manner to boost up the performance, many attempts were delivered considering the combination of different classifiers. SVR (Support Vector Regression) and AdaBoost had contributed in the improvement of the performance however according to Li and Yand (2016); it has been very subjective in manner to capture and detect many features including the texture, shape, color, spatial location and others and was limited for the ambient conditions (Gudigar et al. 2017). Experimental results stated how the natural conditions such as fog, smog, rain, wind and others make the system vulnerable and thus, affecting the efficiency of the project.

Driver Drowsiness Alert System

The considerable areas for the delivery of this project is the consideration of the drowsiness alert system and recognition of the traffic signs as these are the vital factors for the accidents at the roads (Liu et al. 2016). Many researchers had contributed in the identification of these major concerns and tried to eliminate the problem for the enhancement in the efficiency of the ADAS technology. Keser, Kramar and Nozica (2016) proposed the classification based on three different approaches including the psychological, vehicle, and behavior based approaches in manner to identify the factors affecting the overall output of the ADAS technology and driverless vehicles.

The sector of the ADAS covers a vast sector of the technologies those are comprised for this technology and hence, for this paper, two major visions have been considered as explained earlier. This paper will be emphasizing on the drowsiness alert system and “Traffic Sign Recognition System” and following research questions will be addressed in this report:

Question 1: Identification of the factors those have been limiting the application of the ADAS technologies within the automobile sector

Question 2: Identification of the factors and aspects those could be applied for the enhancement of the ADAS performance

Question 3: Identification of the factors those could be applied for enhancing the efficiency of the ADAS system and making it immune from ambient natural conditions.

Question 4: Identification of the aspects those could be applied in ADAS for the enhancement in the computation for the real-time decision-making and deployment of the technology in the real world.

The goal of the report is to make the project much efficient, faster processing, and highly robust in contrast to be applicable in the real world. The answers of the research question will be identified through dividing the whole project in sub-activities that will also be helpful in auditing and monitoring the growth and progress of the report. The list of activities and sub activities along with the start date, finish date, and time required has been presented in the section 7, based on which the Gantt chart has been prepared.  

Consideration of the driver drowsiness and traffic signal recognition has been an important factor in context of the efficiency and effectiveness of the ADAS technology and its application in the real world. The factors affecting the efficiency of this technology can be listed as: bad performance during the variability’s and vulnerabilities in the real world for example graffiti, fading traffic signs’ color fading, physical damage, sun glare, occlusion, jittering of camera, lightning conditions, motion-blur, crafted feature-engineering,  and unwanted natural condition for example wind, fog, rain and others. CNN’s (Convolutional Neural Networks) has been a biologically inspired different layer feed forward neural network architecture that is capable of learning more than one invariant features in relation with the image hierarchy. The first layer is responsible for extracting the local image features, next layer extracts the abstract features, more advanced extracts the smaller objects, and the last layer is responsible for extracting the hierarchical fashion features. Following are the steps of the methodology used for the methodology proposed for this project:

Recognition of Traffic Signs

Dataset collection: the datasets will be downloaded from the datasets applicable publically from different research labs.

Processing the explored dataset: the dataset will be explored prior of the processing in manner to save the time and for future jobs. After ensuring dataset has been done the dataset will be filtered.

Data Augmentation: It will be playing vital role in manner to create the original dataset’s replica in manner to enhance the training data size and equalizing the samples in each class.

Defining CNN Model: Considering the functionality and relevance to the CNN architecture following layers can be recommended as the best approach: Convolution layer, Max-pooling layers, Dropout, regularization, optimizers, and early stopping.

Hyperparameters tuning, virtualization of the model performance, and identification of the results is the last phase of the methodology.   

For the purpose of the evaluation of performance of presented methodology on the alert system for driver drowsiness and recognition of traffic sign, the experimental phase will be accomplished through the application of Torch, Theano, Caffe, Tensorflow, MATLAB, and various wrappers such as Scikit-Learn, Lasagne, and Keras. The list of the tollboxes required for the accomplishment of the experimental setup of MATLAB (2017a) includes “Optimization Toolbox, Image Processing Toolbox, Image Acquisition Toolbox, Computer Vision Systems Toolbox, Statistics and Machine Learning Toolbox, Neural Network Toolbox, and Automated Driving System Toolbox.”

Many standard evaluation techniques can be used for comparing the results on comparing large datasets that is comprised of all unwanted factors however; general evaluation metrics can be executed that consists of classifying recall, precision, F1-score, and accuracy. Following are the formulas for the calculation of these factors:

Recall (R): R = (True positive) / (false positive + true positive)

Precision (P): P = (True positive) / (false positive + true positive)

F-Score (F):  F = (1 + B²) * Recall * Precision / (B² * Precision + Recall)

Accuracy (A) A = (True Negative + True Positive) / (False Positive + True Positive + True Negative + False Negative)

Conclusions

ADAS can be referred to the drivers for better and secured driving in an automated manner and the technology can be applicable in the real world through enhancing the real world practice. There are many beneficial aspects n the application of this technology however; it has not been much efficient and effective in the real world due to many natural phenomenon. In the various conditions, the technology has not been very efficient because of the factors such as rotation, scaling, shearing, blur, shifting, motion, occlusion, and many more. The project was delivered aiming at the elimination of the challenges being identified in the application of the ADAS and improves the efficiency, robustness, and performance in manner to make the technology much reliable for every individual using the ADSAS technology. The report presents a single integrated model that has been working on two different modalities through the addition of other integrated functionalities within a system that result in the reduction of economic cost. This project contributes in the addition of the information within the context of the literature available in the context of the driverless cars.

References

Charalampous, K. & Gasteratos, A. 2013, 'Bio-inspired deep learning model for object recognition', 2013 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 51-5.

Choi, J., Song, E. & Lee, S., 2018, ‘L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification’, Sensors, 18(1), p.306.

Gudigar, A., Chokkadi, S., Raghavendra, U. & Acharya, U.R., 2017, ‘An efficient traffic sign recognition based on graph embedding features. Neural Computing and Applications’, pp.1-13.

Jung, S., Cho, S., Lee, D., Lee, H. & Shim, D.H., 2018, ‘A direct visual servoing?based framework for the 2016 IROS Autonomous Drone Racing Challenge’, Journal of Field Robotics, 35(1), pp.146-166.

Kale, A.J. & Mahajan, R.C., 2015, October, ‘A road sign detection and the recognition for Driver Assistance Systems. In Energy Systems and Applications’, 2015 International Conference on (pp. 69-74). IEEE.

Keser, T., Kramar, G. & Nožica, D., 2016, October, ‘Traffic signs shape recognition based on contour descriptor analysis. In Smart Systems and Technologies (SST)’, International Conference on (pp. 199-204). IEEE.

Li, C. & Yang, C., 2016, September, ‘The research on traffic sign recognition based on deep learning. In Communications and Information Technologies (ISCIT)’, 2016 16th International Symposium on (pp. 156-161). IEEE.

Liu, H., Stoll, N., Junginger, S., Zhang, J., Ghandour, M. & Thurow, K., 2016, July, ‘Human-Mobile Robot Interaction in laboratories using Kinect Sensor and ELM based face feature recognition. In Human System Interactions (HSI)’, 2016 9th International Conference on (pp. 197-202). IEEE.

Nguyen, B.T., Ryong, S.J. & Kyu, K.J., 2014, July, ‘Fast traffic sign detection under challenging conditions. In Audio, Language and Image Processing (ICALIP)’, 2014 International Conference on (pp. 749-752). IEEE.

Sun, X., Liu, L., Wang, H., Song, W. and Lu, J., 2015, December, ‘Image classification via support vector machine. In Computer Science and Network Technology (ICCSNT)’, 2015 4th International Conference on (Vol. 1, pp. 485-489). IEEE.

Villalón-Sepúlveda, G., Torres-Torriti, M. & Flores-Calero, M., 2017, ‘Traffic sign detection system for locating road intersections and roundabouts: the Chilean case’, Sensors, 17(6), p.1207.

Zeng, Y., Xu, X., Fang, Y. & Zhao, K., 2015, ‘Traffic sign recognition using deep convolutional networks and extreme learning machine in Intelligence Science and Big Data Engineering’, Image and Video Data Engineering. In 5th International Conference. IScIDE.